--- layout: documentation title: Malloy Documentation footer: /generated/footers/examples/names.html ---

Name Game

You can find the complete source code for this model here.

The Data set consists of name, gender, state and year with the number of people that were born with that name in that gender, state and year.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  top: 10
  project: *
}
QUERY RESULTS
stategenderyearnamenumber
AKF1,910Elsie6
ALF1,910Annie482
ALF1,910Katie84
ALF1,910Alma82
ALF1,910Thelma65
ALF1,910Ellen38
ALF1,910Rebecca35
ALF1,910Estella21
ALF1,910Corine17
ALF1,910Margie17
[
  {
    "state": "AK",
    "gender": "F",
    "year": 1910,
    "name": "Elsie",
    "number": 6
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Annie",
    "number": 482
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Katie",
    "number": 84
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Alma",
    "number": 82
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Thelma",
    "number": 65
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Ellen",
    "number": 38
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Rebecca",
    "number": 35
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Estella",
    "number": 21
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Corine",
    "number": 17
  },
  {
    "state": "AL",
    "gender": "F",
    "year": 1910,
    "name": "Margie",
    "number": 17
  }
]
SELECT 
   base.state as state,
   base.gender as gender,
   base.year as year,
   base.name as name,
   base.number as number
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
LIMIT 10

The command above says query the table 'bigquery-public-data.usa_names.usa_1910_2013' and project (show) all the columns for the first 10 rows.

Grouping a query

In SQL there are basically two kinds of SELECT commands: SELECT ... GROUP BY and SELECT without a grouping. In Malloy, these are two different commands. The command in Malloy for SELECT ... GROUP BY is group_by. Since number and year are reserved words, we have to quote the names with back-tics.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  group_by: name
  aggregate: population is `number`.sum()
}
QUERY RESULTS
namepopulation
James4,942,431
John4,834,422
Robert4,718,787
Michael4,297,230
William3,822,209
Mary3,737,679
David3,549,801
Richard2,531,924
Joseph2,472,917
Charles2,244,693
Thomas2,212,781
Christopher1,987,490
Daniel1,842,964
Patricia1,569,570
Matthew1,538,689
Elizabeth1,492,404
Jennifer1,463,145
Linda1,447,601
Barbara1,424,203
Donald1,402,666
Anthony1,385,453
Paul1,355,711
Mark1,339,371
George1,321,652
Steven1,269,627
Kenneth1,259,720
Andrew1,228,080
Edward1,210,315
Joshua1,163,481
Brian1,156,495
Kevin1,148,680
Margaret1,120,766
Susan1,108,712
Ronald1,074,141
Timothy1,055,792
Dorothy1,052,570
Jessica1,038,359
Jason1,009,416
Sarah1,006,934
Betty985,364
Nancy984,185
Karen982,192
Jeffrey969,093
Lisa962,981
Ryan907,969
Gary896,637
Helen895,428
Sandra870,958
Nicholas867,231
Eric862,204
Jacob848,518
Stephen844,930
Ashley841,427
Donna826,714
Kimberly826,275
Frank818,197
Carol817,290
Jonathan803,723
Michelle803,162
Larry801,398
Emily779,755
Amanda769,292
Scott765,844
Justin757,273
Laura745,892
Melissa745,498
Raymond744,284
Deborah735,869
Brandon735,849
Stephanie732,651
Rebecca728,799
Ruth728,678
Anna719,803
Sharon718,701
Kathleen703,265
Cynthia703,175
Gregory701,504
Shirley686,079
Samuel681,358
Amy673,234
Benjamin664,328
Patrick655,434
Angela653,663
Jack634,113
Virginia621,448
Jerry618,105
Dennis610,894
Catherine606,029
Brenda604,067
Alexander598,944
Katherine593,220
Pamela591,307
Nicole576,870
Tyler576,031
Christine574,120
Henry571,539
Walter558,026
Peter553,789
Douglas552,666
Janet550,557
[
  {
    "name": "James",
    "population": 4942431
  },
  {
    "name": "John",
    "population": 4834422
  },
  {
    "name": "Robert",
    "population": 4718787
  },
  {
    "name": "Michael",
    "population": 4297230
  },
  {
    "name": "William",
    "population": 3822209
  },
  {
    "name": "Mary",
    "population": 3737679
  },
  {
    "name": "David",
    "population": 3549801
  },
  {
    "name": "Richard",
    "population": 2531924
  },
  {
    "name": "Joseph",
    "population": 2472917
  },
  {
    "name": "Charles",
    "population": 2244693
  },
  {
    "name": "Thomas",
    "population": 2212781
  },
  {
    "name": "Christopher",
    "population": 1987490
  },
  {
    "name": "Daniel",
    "population": 1842964
  },
  {
    "name": "Patricia",
    "population": 1569570
  },
  {
    "name": "Matthew",
    "population": 1538689
  },
  {
    "name": "Elizabeth",
    "population": 1492404
  },
  {
    "name": "Jennifer",
    "population": 1463145
  },
  {
    "name": "Linda",
    "population": 1447601
  },
  {
    "name": "Barbara",
    "population": 1424203
  },
  {
    "name": "Donald",
    "population": 1402666
  },
  {
    "name": "Anthony",
    "population": 1385453
  },
  {
    "name": "Paul",
    "population": 1355711
  },
  {
    "name": "Mark",
    "population": 1339371
  },
  {
    "name": "George",
    "population": 1321652
  },
  {
    "name": "Steven",
    "population": 1269627
  },
  {
    "name": "Kenneth",
    "population": 1259720
  },
  {
    "name": "Andrew",
    "population": 1228080
  },
  {
    "name": "Edward",
    "population": 1210315
  },
  {
    "name": "Joshua",
    "population": 1163481
  },
  {
    "name": "Brian",
    "population": 1156495
  },
  {
    "name": "Kevin",
    "population": 1148680
  },
  {
    "name": "Margaret",
    "population": 1120766
  },
  {
    "name": "Susan",
    "population": 1108712
  },
  {
    "name": "Ronald",
    "population": 1074141
  },
  {
    "name": "Timothy",
    "population": 1055792
  },
  {
    "name": "Dorothy",
    "population": 1052570
  },
  {
    "name": "Jessica",
    "population": 1038359
  },
  {
    "name": "Jason",
    "population": 1009416
  },
  {
    "name": "Sarah",
    "population": 1006934
  },
  {
    "name": "Betty",
    "population": 985364
  },
  {
    "name": "Nancy",
    "population": 984185
  },
  {
    "name": "Karen",
    "population": 982192
  },
  {
    "name": "Jeffrey",
    "population": 969093
  },
  {
    "name": "Lisa",
    "population": 962981
  },
  {
    "name": "Ryan",
    "population": 907969
  },
  {
    "name": "Gary",
    "population": 896637
  },
  {
    "name": "Helen",
    "population": 895428
  },
  {
    "name": "Sandra",
    "population": 870958
  },
  {
    "name": "Nicholas",
    "population": 867231
  },
  {
    "name": "Eric",
    "population": 862204
  },
  {
    "name": "Jacob",
    "population": 848518
  },
  {
    "name": "Stephen",
    "population": 844930
  },
  {
    "name": "Ashley",
    "population": 841427
  },
  {
    "name": "Donna",
    "population": 826714
  },
  {
    "name": "Kimberly",
    "population": 826275
  },
  {
    "name": "Frank",
    "population": 818197
  },
  {
    "name": "Carol",
    "population": 817290
  },
  {
    "name": "Jonathan",
    "population": 803723
  },
  {
    "name": "Michelle",
    "population": 803162
  },
  {
    "name": "Larry",
    "population": 801398
  },
  {
    "name": "Emily",
    "population": 779755
  },
  {
    "name": "Amanda",
    "population": 769292
  },
  {
    "name": "Scott",
    "population": 765844
  },
  {
    "name": "Justin",
    "population": 757273
  },
  {
    "name": "Laura",
    "population": 745892
  },
  {
    "name": "Melissa",
    "population": 745498
  },
  {
    "name": "Raymond",
    "population": 744284
  },
  {
    "name": "Deborah",
    "population": 735869
  },
  {
    "name": "Brandon",
    "population": 735849
  },
  {
    "name": "Stephanie",
    "population": 732651
  },
  {
    "name": "Rebecca",
    "population": 728799
  },
  {
    "name": "Ruth",
    "population": 728678
  },
  {
    "name": "Anna",
    "population": 719803
  },
  {
    "name": "Sharon",
    "population": 718701
  },
  {
    "name": "Kathleen",
    "population": 703265
  },
  {
    "name": "Cynthia",
    "population": 703175
  },
  {
    "name": "Gregory",
    "population": 701504
  },
  {
    "name": "Shirley",
    "population": 686079
  },
  {
    "name": "Samuel",
    "population": 681358
  },
  {
    "name": "Amy",
    "population": 673234
  },
  {
    "name": "Benjamin",
    "population": 664328
  },
  {
    "name": "Patrick",
    "population": 655434
  },
  {
    "name": "Angela",
    "population": 653663
  },
  {
    "name": "Jack",
    "population": 634113
  },
  {
    "name": "Virginia",
    "population": 621448
  },
  {
    "name": "Jerry",
    "population": 618105
  },
  {
    "name": "Dennis",
    "population": 610894
  },
  {
    "name": "Catherine",
    "population": 606029
  },
  {
    "name": "Brenda",
    "population": 604067
  },
  {
    "name": "Alexander",
    "population": 598944
  },
  {
    "name": "Katherine",
    "population": 593220
  },
  {
    "name": "Pamela",
    "population": 591307
  },
  {
    "name": "Nicole",
    "population": 576870
  },
  {
    "name": "Tyler",
    "population": 576031
  },
  {
    "name": "Christine",
    "population": 574120
  },
  {
    "name": "Henry",
    "population": 571539
  },
  {
    "name": "Walter",
    "population": 558026
  },
  {
    "name": "Peter",
    "population": 553789
  },
  {
    "name": "Douglas",
    "population": 552666
  },
  {
    "name": "Janet",
    "population": 550557
  }
]
SELECT 
   base.name as name,
   COALESCE(SUM(base.number),0) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc

Malloy compiles to SQL. The SQL query for the above command is.

SELECT
   base.name as name,
   SUM(base.number) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc

Expressions

Expressions work much the same as they do in SQL. We can look at population over decade by using a calculation against the year.

query: table('bigquery-public-data.usa_names.usa_1910_2013') -> {
  top: 10
  group_by: decade is floor(`year` / 10) * 10
  aggregate: population is sum(`number`)
}
QUERY RESULTS
decadepopulation
1,95037,545,138
1,96035,405,002
1,99033,309,594
2,00033,041,629
1,98032,349,146
1,97029,238,092
1,94027,776,913
1,92021,375,400
1,93019,782,373
1,91013,592,350
[
  {
    "decade": 1950,
    "population": 37545138
  },
  {
    "decade": 1960,
    "population": 35405002
  },
  {
    "decade": 1990,
    "population": 33309594
  },
  {
    "decade": 2000,
    "population": 33041629
  },
  {
    "decade": 1980,
    "population": 32349146
  },
  {
    "decade": 1970,
    "population": 29238092
  },
  {
    "decade": 1940,
    "population": 27776913
  },
  {
    "decade": 1920,
    "population": 21375400
  },
  {
    "decade": 1930,
    "population": 19782373
  },
  {
    "decade": 1910,
    "population": 13592350
  }
]
SELECT 
   (floor(base.year/10))*10 as decade,
   COALESCE(SUM(base.number),0) as population
FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
GROUP BY 1
ORDER BY 2 desc
LIMIT 10

Cohorts 

source: names is from(
  table('bigquery-public-data.usa_names.usa_1910_2013') -> {
    group_by:
      decade is floor(`year` / 10) * 10
      state
      gender
    aggregate: cohort_population is `number`.sum()
    nest: by_name is {
      group_by: name
      aggregate: population is `number`.sum()
    }
  }
) {
  measure: births_per_100k is floor(by_name.population.sum() / cohort_population.sum() * 100000)
  measure: total_cohort_population is cohort_population.sum()
  dimension: name is concat(by_name.name, '')
}

query: names -> {
  group_by:
    state
    gender
  aggregate: total_cohort_population
  nest: names is {
    where: name  ? 'Michael' | 'Lloyd' | 'Olivia'
    group_by: name
    aggregate: population is by_name.population.sum()
    group_by: births_per_100k
  }
}
QUERY RESULTS
stategendertotal_​cohort_​populationnames
CAM15,011,405
namepopulationbirths_​per_​100k
Michael420,1272,798
Lloyd8,38055
CAF13,799,158
namepopulationbirths_​per_​100k
Olivia33,794244
Michael2,42819
NYM12,203,961
namepopulationbirths_​per_​100k
Michael430,7323,529
Lloyd7,56862
Olivia50
NYF11,490,175
namepopulationbirths_​per_​100k
Olivia23,219202
Michael1,76116
TXM11,126,246
namepopulationbirths_​per_​100k
Michael223,9002,012
Lloyd8,46176
Olivia61
TXF10,341,113
namepopulationbirths_​per_​100k
Olivia25,746248
Michael1,42016
Lloyd151
PAM8,497,961
namepopulationbirths_​per_​100k
Michael266,3803,134
Lloyd7,87492
Olivia222
PAF8,163,949
namepopulationbirths_​per_​100k
Olivia16,292199
Michael91011
ILM7,845,252
namepopulationbirths_​per_​100k
Michael251,7413,208
Lloyd7,20791
ILF7,333,624
namepopulationbirths_​per_​100k
Olivia16,450224
Michael98917
OHM7,324,810
namepopulationbirths_​per_​100k
Michael225,3023,075
Lloyd6,75592
OHF6,876,716
namepopulationbirths_​per_​100k
Olivia16,787244
Michael96918
MIM6,036,661
namepopulationbirths_​per_​100k
Michael194,8513,227
Lloyd6,130101
MIF5,606,794
namepopulationbirths_​per_​100k
Olivia13,575242
Michael75517
FLM4,910,950
namepopulationbirths_​per_​100k
Michael138,8822,828
Lloyd2,87658
FLF4,366,273
namepopulationbirths_​per_​100k
Olivia15,208348
Michael69118
NCM4,326,810
namepopulationbirths_​per_​100k
Michael92,4142,135
Lloyd4,367100
NJM4,315,822
namepopulationbirths_​per_​100k
Michael165,3853,832
Lloyd1,72041
GAM4,214,825
namepopulationbirths_​per_​100k
Michael87,6842,080
Lloyd2,50859
NCF4,113,425
namepopulationbirths_​per_​100k
Olivia10,671259
Michael41619
[
  {
    "state": "CA",
    "gender": "M",
    "total_cohort_population": 15011405,
    "names": [
      {
        "name": "Michael",
        "population": 420127,
        "births_per_100k": 2798
      },
      {
        "name": "Lloyd",
        "population": 8380,
        "births_per_100k": 55
      }
    ]
  },
  {
    "state": "CA",
    "gender": "F",
    "total_cohort_population": 13799158,
    "names": [
      {
        "name": "Olivia",
        "population": 33794,
        "births_per_100k": 244
      },
      {
        "name": "Michael",
        "population": 2428,
        "births_per_100k": 19
      }
    ]
  },
  {
    "state": "NY",
    "gender": "M",
    "total_cohort_population": 12203961,
    "names": [
      {
        "name": "Michael",
        "population": 430732,
        "births_per_100k": 3529
      },
      {
        "name": "Lloyd",
        "population": 7568,
        "births_per_100k": 62
      },
      {
        "name": "Olivia",
        "population": 5,
        "births_per_100k": 0
      }
    ]
  },
  {
    "state": "NY",
    "gender": "F",
    "total_cohort_population": 11490175,
    "names": [
      {
        "name": "Olivia",
        "population": 23219,
        "births_per_100k": 202
      },
      {
        "name": "Michael",
        "population": 1761,
        "births_per_100k": 16
      }
    ]
  },
  {
    "state": "TX",
    "gender": "M",
    "total_cohort_population": 11126246,
    "names": [
      {
        "name": "Michael",
        "population": 223900,
        "births_per_100k": 2012
      },
      {
        "name": "Lloyd",
        "population": 8461,
        "births_per_100k": 76
      },
      {
        "name": "Olivia",
        "population": 6,
        "births_per_100k": 1
      }
    ]
  },
  {
    "state": "TX",
    "gender": "F",
    "total_cohort_population": 10341113,
    "names": [
      {
        "name": "Olivia",
        "population": 25746,
        "births_per_100k": 248
      },
      {
        "name": "Michael",
        "population": 1420,
        "births_per_100k": 16
      },
      {
        "name": "Lloyd",
        "population": 15,
        "births_per_100k": 1
      }
    ]
  },
  {
    "state": "PA",
    "gender": "M",
    "total_cohort_population": 8497961,
    "names": [
      {
        "name": "Michael",
        "population": 266380,
        "births_per_100k": 3134
      },
      {
        "name": "Lloyd",
        "population": 7874,
        "births_per_100k": 92
      },
      {
        "name": "Olivia",
        "population": 22,
        "births_per_100k": 2
      }
    ]
  },
  {
    "state": "PA",
    "gender": "F",
    "total_cohort_population": 8163949,
    "names": [
      {
        "name": "Olivia",
        "population": 16292,
        "births_per_100k": 199
      },
      {
        "name": "Michael",
        "population": 910,
        "births_per_100k": 11
      }
    ]
  },
  {
    "state": "IL",
    "gender": "M",
    "total_cohort_population": 7845252,
    "names": [
      {
        "name": "Michael",
        "population": 251741,
        "births_per_100k": 3208
      },
      {
        "name": "Lloyd",
        "population": 7207,
        "births_per_100k": 91
      }
    ]
  },
  {
    "state": "IL",
    "gender": "F",
    "total_cohort_population": 7333624,
    "names": [
      {
        "name": "Olivia",
        "population": 16450,
        "births_per_100k": 224
      },
      {
        "name": "Michael",
        "population": 989,
        "births_per_100k": 17
      }
    ]
  },
  {
    "state": "OH",
    "gender": "M",
    "total_cohort_population": 7324810,
    "names": [
      {
        "name": "Michael",
        "population": 225302,
        "births_per_100k": 3075
      },
      {
        "name": "Lloyd",
        "population": 6755,
        "births_per_100k": 92
      }
    ]
  },
  {
    "state": "OH",
    "gender": "F",
    "total_cohort_population": 6876716,
    "names": [
      {
        "name": "Olivia",
        "population": 16787,
        "births_per_100k": 244
      },
      {
        "name": "Michael",
        "population": 969,
        "births_per_100k": 18
      }
    ]
  },
  {
    "state": "MI",
    "gender": "M",
    "total_cohort_population": 6036661,
    "names": [
      {
        "name": "Michael",
        "population": 194851,
        "births_per_100k": 3227
      },
      {
        "name": "Lloyd",
        "population": 6130,
        "births_per_100k": 101
      }
    ]
  },
  {
    "state": "MI",
    "gender": "F",
    "total_cohort_population": 5606794,
    "names": [
      {
        "name": "Olivia",
        "population": 13575,
        "births_per_100k": 242
      },
      {
        "name": "Michael",
        "population": 755,
        "births_per_100k": 17
      }
    ]
  },
  {
    "state": "FL",
    "gender": "M",
    "total_cohort_population": 4910950,
    "names": [
      {
        "name": "Michael",
        "population": 138882,
        "births_per_100k": 2828
      },
      {
        "name": "Lloyd",
        "population": 2876,
        "births_per_100k": 58
      }
    ]
  },
  {
    "state": "FL",
    "gender": "F",
    "total_cohort_population": 4366273,
    "names": [
      {
        "name": "Olivia",
        "population": 15208,
        "births_per_100k": 348
      },
      {
        "name": "Michael",
        "population": 691,
        "births_per_100k": 18
      }
    ]
  },
  {
    "state": "NC",
    "gender": "M",
    "total_cohort_population": 4326810,
    "names": [
      {
        "name": "Michael",
        "population": 92414,
        "births_per_100k": 2135
      },
      {
        "name": "Lloyd",
        "population": 4367,
        "births_per_100k": 100
      }
    ]
  },
  {
    "state": "NJ",
    "gender": "M",
    "total_cohort_population": 4315822,
    "names": [
      {
        "name": "Michael",
        "population": 165385,
        "births_per_100k": 3832
      },
      {
        "name": "Lloyd",
        "population": 1720,
        "births_per_100k": 41
      }
    ]
  },
  {
    "state": "GA",
    "gender": "M",
    "total_cohort_population": 4214825,
    "names": [
      {
        "name": "Michael",
        "population": 87684,
        "births_per_100k": 2080
      },
      {
        "name": "Lloyd",
        "population": 2508,
        "births_per_100k": 59
      }
    ]
  },
  {
    "state": "NC",
    "gender": "F",
    "total_cohort_population": 4113425,
    "names": [
      {
        "name": "Olivia",
        "population": 10671,
        "births_per_100k": 259
      },
      {
        "name": "Michael",
        "population": 416,
        "births_per_100k": 19
      }
    ]
  }
]
WITH __stage0 AS (
  SELECT
    group_set,
    CASE WHEN group_set IN (0,1) THEN
      (floor(base.year/10))*10
      END as decade__0,
    CASE WHEN group_set IN (0,1) THEN
      base.state
      END as state__0,
    CASE WHEN group_set IN (0,1) THEN
      base.gender
      END as gender__0,
    CASE WHEN group_set=0 THEN
      COALESCE(SUM(base.number),0)
      END as cohort_population__0,
    CASE WHEN group_set=1 THEN
      base.name
      END as name__1,
    CASE WHEN group_set=1 THEN
      COALESCE(SUM(base.number),0)
      END as population__1
  FROM `bigquery-public-data.usa_names.usa_1910_2013` as base
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  GROUP BY 1,2,3,4,6
)
, __stage1 AS (
  SELECT
    decade__0 as decade,
    state__0 as state,
    gender__0 as gender,
    ANY_VALUE(CASE WHEN group_set=0 THEN cohort_population__0 END) as cohort_population,
    ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
      name__1 as name, 
      population__1 as population
      ) END IGNORE NULLS  ORDER BY  population__1 desc ) as by_name
  FROM __stage0
  GROUP BY 1,2,3
  ORDER BY 4 desc
)
, __stage2 AS (
  SELECT
    group_set,
    CASE WHEN group_set IN (0,1) THEN
      names.state
      END as state__0,
    CASE WHEN group_set IN (0,1) THEN
      names.gender
      END as gender__0,
    CASE WHEN group_set=0 THEN
      COALESCE(CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64),0)
      END as total_cohort_population__0,
    CASE WHEN group_set=1 THEN
      concat(by_name_0.name,'')
      END as name__1,
    CASE WHEN group_set=1 THEN
      COALESCE(SUM(by_name_0.population),0)
      END as population__1,
    floor((CASE WHEN group_set=1 THEN
      COALESCE(SUM(by_name_0.population),0)
      END/CASE WHEN group_set=1 THEN
      COALESCE(CAST((
      (
        SUM(DISTINCT
          (CAST(ROUND(COALESCE(names.cohort_population,0)*(1*1.0), 9) AS NUMERIC) +
          (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001
        ))
        -
         SUM(DISTINCT (cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 1, 15)) as int64) as numeric) * 4294967296 + cast(cast(concat('0x', substr(to_hex(md5(CAST(names.__distinct_key AS STRING))), 16, 8)) as int64) as numeric)) * 0.000000001)
      )/(1*1.0)) as FLOAT64),0)
      END)*100000) as births_per_100k__1
  FROM (SELECT GENERATE_UUID() as __distinct_key, * FROM __stage1 as x) as names
  LEFT JOIN UNNEST(names.by_name) as by_name_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,1,1)))
  WHERE (group_set NOT IN (1) OR (group_set IN (1) AND ((concat(by_name_0.name,''))='Michael')or(((concat(by_name_0.name,''))='Lloyd')or((concat(by_name_0.name,''))='Olivia'))))
  GROUP BY 1,2,3,5
)
SELECT
  state__0 as state,
  gender__0 as gender,
  ANY_VALUE(CASE WHEN group_set=0 THEN total_cohort_population__0 END) as total_cohort_population,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
    name__1 as name, 
    population__1 as population, 
    births_per_100k__1 as births_per_100k
    ) END IGNORE NULLS  ORDER BY  population__1 desc ) as names
FROM __stage2
GROUP BY 1,2
ORDER BY 3 desc

Dashboard 

query: names -> name_dashboard { where: name ~ 'Mich%' }
QUERY RESULTS
nametotal_​populationstate_​mapdecade_​graph
Michael4,396,482
2,0004,0006,0008,00010,000births_per_100k
1,9201,9401,9601,9802,0002,020decade05,00010,00015,00020,000births_per_100kFMgender
Michelle813,655
17714,069births_per_100k
1,9201,9401,9601,9802,0002,020decade010,00020,00030,000births_per_100kFMgender
Michele221,811
5,00010,00015,000births_per_100k
1,9201,9401,9601,9802,0002,020decade010,00020,00030,00040,000births_per_100kFMgender
Micheal150,977
2,0004,0006,0008,000births_per_100k
1,9201,9401,9601,9802,0002,020decade010,00020,000births_per_100kFMgender
Michaela48,892
2,0004,0006,0008,00010,000births_per_100k
1,9401,9601,9802,0002,020decade020,00040,000births_per_100kFMgender
Michel7,062
5,00010,00015,00020,00025,000births_per_100k
1,9201,9401,9601,9802,0002,020decade010,00020,000births_per_100kFMgender
Michell4,377
5,00010,00015,00020,000births_per_100k
1,9601,9802,000decade010,00020,00030,00040,000births_per_100kFMgender
Michal2,402
10,00020,00030,000births_per_100k
1,9401,9601,9802,0002,020decade05,00010,000births_per_100kFMgender
Michale954
10,00020,00030,000births_per_100k
1,9601,9802,000decade010,00020,00030,000births_per_100kMgender
Michelina909
20,00040,00060,000births_per_100k
1,9201,9401,9601,9802,0002,020decade010,00020,00030,00040,000births_per_100kFgender
Michaella711
5,00010,00015,00020,00025,000births_per_100k
1,9601,9802,0002,020decade020,00040,00060,000births_per_100kFgender
Michela621
10,00020,00030,000births_per_100k
1,9401,9601,9802,0002,020decade020,00040,000births_per_100kFgender
Michaelangelo579
20,00040,00060,000births_per_100k
1,9701,9801,9902,0002,0102,020decade010,00020,00030,000births_per_100kMgender
Michaelene477
10,00020,00030,00040,000births_per_100k
1,9301,9401,9501,9601,9701,980decade020,00040,000births_per_100kFgender
Michala465
2,0004,0006,0008,00010,00012,000births_per_100k
1,9701,9801,9902,000decade020,00040,00060,00080,000births_per_100kFgender
[
  {
    "name": "Michael",
    "total_population": 4396482,
    "state_map": [
      {
        "state": "NY",
        "births_per_100k": 10019
      },
      {
        "state": "CA",
        "births_per_100k": 9886
      },
      {
        "state": "PA",
        "births_per_100k": 6181
      },
      {
        "state": "IL",
        "births_per_100k": 5844
      },
      {
        "state": "TX",
        "births_per_100k": 5340
      },
      {
        "state": "OH",
        "births_per_100k": 5228
      },
      {
        "state": "MI",
        "births_per_100k": 4513
      },
      {
        "state": "NJ",
        "births_per_100k": 3859
      },
      {
        "state": "FL",
        "births_per_100k": 3324
      },
      {
        "state": "MA",
        "births_per_100k": 3310
      },
      {
        "state": "IN",
        "births_per_100k": 2514
      },
      {
        "state": "MO",
        "births_per_100k": 2202
      },
      {
        "state": "WI",
        "births_per_100k": 2195
      },
      {
        "state": "NC",
        "births_per_100k": 2176
      },
      {
        "state": "GA",
        "births_per_100k": 2073
      },
      {
        "state": "VA",
        "births_per_100k": 2063
      },
      {
        "state": "MN",
        "births_per_100k": 1876
      },
      {
        "state": "MD",
        "births_per_100k": 1776
      },
      {
        "state": "TN",
        "births_per_100k": 1689
      },
      {
        "state": "WA",
        "births_per_100k": 1610
      },
      {
        "state": "CT",
        "births_per_100k": 1585
      },
      {
        "state": "LA",
        "births_per_100k": 1506
      },
      {
        "state": "KY",
        "births_per_100k": 1436
      },
      {
        "state": "AL",
        "births_per_100k": 1337
      },
      {
        "state": "IA",
        "births_per_100k": 1234
      },
      {
        "state": "CO",
        "births_per_100k": 1132
      },
      {
        "state": "OK",
        "births_per_100k": 1070
      },
      {
        "state": "AZ",
        "births_per_100k": 1064
      },
      {
        "state": "SC",
        "births_per_100k": 1043
      },
      {
        "state": "KS",
        "births_per_100k": 951
      },
      {
        "state": "OR",
        "births_per_100k": 918
      },
      {
        "state": "MS",
        "births_per_100k": 810
      },
      {
        "state": "WV",
        "births_per_100k": 787
      },
      {
        "state": "AR",
        "births_per_100k": 738
      },
      {
        "state": "NE",
        "births_per_100k": 694
      },
      {
        "state": "DC",
        "births_per_100k": 674
      },
      {
        "state": "UT",
        "births_per_100k": 669
      },
      {
        "state": "RI",
        "births_per_100k": 570
      },
      {
        "state": "NM",
        "births_per_100k": 513
      },
      {
        "state": "ME",
        "births_per_100k": 478
      },
      {
        "state": "NH",
        "births_per_100k": 380
      },
      {
        "state": "ID",
        "births_per_100k": 344
      },
      {
        "state": "NV",
        "births_per_100k": 326
      },
      {
        "state": "HI",
        "births_per_100k": 317
      },
      {
        "state": "MT",
        "births_per_100k": 299
      },
      {
        "state": "SD",
        "births_per_100k": 294
      },
      {
        "state": "DE",
        "births_per_100k": 292
      },
      {
        "state": "ND",
        "births_per_100k": 279
      },
      {
        "state": "VT",
        "births_per_100k": 206
      },
      {
        "state": "AK",
        "births_per_100k": 188
      },
      {
        "state": "WY",
        "births_per_100k": 164
      }
    ],
    "decade_graph": [
      {
        "decade": 1910,
        "births_per_100k": 693,
        "gender": "M"
      },
      {
        "decade": 1910,
        "births_per_100k": 0,
        "gender": "F"
      },
      {
        "decade": 1920,
        "births_per_100k": 856,
        "gender": "M"
      },
      {
        "decade": 1920,
        "births_per_100k": 2,
        "gender": "F"
      },
      {
        "decade": 1930,
        "births_per_100k": 1271,
        "gender": "M"
      },
      {
        "decade": 1930,
        "births_per_100k": 2,
        "gender": "F"
      },
      {
        "decade": 1940,
        "births_per_100k": 21,
        "gender": "F"
      },
      {
        "decade": 1940,
        "births_per_100k": 7654,
        "gender": "M"
      },
      {
        "decade": 1950,
        "births_per_100k": 19046,
        "gender": "M"
      },
      {
        "decade": 1950,
        "births_per_100k": 38,
        "gender": "F"
      },
      {
        "decade": 1960,
        "births_per_100k": 72,
        "gender": "F"
      },
      {
        "decade": 1960,
        "births_per_100k": 18950,
        "gender": "M"
      },
      {
        "decade": 1970,
        "births_per_100k": 107,
        "gender": "F"
      },
      {
        "decade": 1970,
        "births_per_100k": 16094,
        "gender": "M"
      },
      {
        "decade": 1980,
        "births_per_100k": 108,
        "gender": "F"
      },
      {
        "decade": 1980,
        "births_per_100k": 15099,
        "gender": "M"
      },
      {
        "decade": 1990,
        "births_per_100k": 30,
        "gender": "F"
      },
      {
        "decade": 1990,
        "births_per_100k": 10517,
        "gender": "M"
      },
      {
        "decade": 2000,
        "births_per_100k": 5700,
        "gender": "M"
      },
      {
        "decade": 2000,
        "births_per_100k": 7,
        "gender": "F"
      },
      {
        "decade": 2010,
        "births_per_100k": 0,
        "gender": "F"
      },
      {
        "decade": 2010,
        "births_per_100k": 3295,
        "gender": "M"
      },
      {
        "decade": 2020,
        "births_per_100k": 427,
        "gender": "M"
      }
    ]
  },
  {
    "name": "Michelle",
    "total_population": 813655,
    "state_map": [
      {
        "state": "CA",
        "births_per_100k": 14069
      },
      {
        "state": "NY",
        "births_per_100k": 8549
      },
      {
        "state": "TX",
        "births_per_100k": 6012
      },
      {
        "state": "IL",
        "births_per_100k": 5858
      },
      {
        "state": "OH",
        "births_per_100k": 5434
      },
      {
        "state": "PA",
        "births_per_100k": 4930
      },
      {
        "state": "MI",
        "births_per_100k": 4880
      },
      {
        "state": "FL",
        "births_per_100k": 3644
      },
      {
        "state": "NJ",
        "births_per_100k": 3091
      },
      {
        "state": "MA",
        "births_per_100k": 3069
      },
      {
        "state": "IN",
        "births_per_100k": 2544
      },
      {
        "state": "WI",
        "births_per_100k": 2302
      },
      {
        "state": "MO",
        "births_per_100k": 2183
      },
      {
        "state": "MN",
        "births_per_100k": 2134
      },
      {
        "state": "VA",
        "births_per_100k": 1897
      },
      {
        "state": "WA",
        "births_per_100k": 1877
      },
      {
        "state": "MD",
        "births_per_100k": 1617
      },
      {
        "state": "LA",
        "births_per_100k": 1557
      },
      {
        "state": "NC",
        "births_per_100k": 1514
      },
      {
        "state": "GA",
        "births_per_100k": 1483
      },
      {
        "state": "CO",
        "births_per_100k": 1467
      },
      {
        "state": "IA",
        "births_per_100k": 1419
      },
      {
        "state": "AZ",
        "births_per_100k": 1417
      },
      {
        "state": "CT",
        "births_per_100k": 1264
      },
      {
        "state": "KS",
        "births_per_100k": 1116
      },
      {
        "state": "OR",
        "births_per_100k": 1096
      },
      {
        "state": "KY",
        "births_per_100k": 1031
      },
      {
        "state": "TN",
        "births_per_100k": 1027
      },
      {
        "state": "UT",
        "births_per_100k": 955
      },
      {
        "state": "OK",
        "births_per_100k": 927
      },
      {
        "state": "NE",
        "births_per_100k": 853
      },
      {
        "state": "AL",
        "births_per_100k": 716
      },
      {
        "state": "SC",
        "births_per_100k": 712
      },
      {
        "state": "NM",
        "births_per_100k": 660
      },
      {
        "state": "DC",
        "births_per_100k": 642
      },
      {
        "state": "WV",
        "births_per_100k": 607
      },
      {
        "state": "ME",
        "births_per_100k": 520
      },
      {
        "state": "AR",
        "births_per_100k": 501
      },
      {
        "state": "RI",
        "births_per_100k": 479
      },
      {
        "state": "HI",
        "births_per_100k": 446
      },
      {
        "state": "MS",
        "births_per_100k": 427
      },
      {
        "state": "NV",
        "births_per_100k": 409
      },
      {
        "state": "ID",
        "births_per_100k": 397
      },
      {
        "state": "NH",
        "births_per_100k": 369
      },
      {
        "state": "ND",
        "births_per_100k": 358
      },
      {
        "state": "SD",
        "births_per_100k": 339
      },
      {
        "state": "MT",
        "births_per_100k": 319
      },
      {
        "state": "DE",
        "births_per_100k": 262
      },
      {
        "state": "AK",
        "births_per_100k": 224
      },
      {
        "state": "VT",
        "births_per_100k": 195
      },
      {
        "state": "WY",
        "births_per_100k": 177
      }
    ],
    "decade_graph": [
      {
        "decade": 1920,
        "births_per_100k": 1,
        "gender": "F"
      },
      {
        "decade": 1930,
        "births_per_100k": 12,
        "gender": "F"
      },
      {
        "decade": 1940,
        "births_per_100k": 641,
        "gender": "F"
      },
      {
        "decade": 1950,
        "births_per_100k": 5742,
        "gender": "F"
      },
      {
        "decade": 1960,
        "births_per_100k": 25599,
        "gender": "F"
      },
      {
        "decade": 1960,
        "births_per_100k": 39,
        "gender": "M"
      },
      {
        "decade": 1970,
        "births_per_100k": 30620,
        "gender": "F"
      },
      {
        "decade": 1970,
        "births_per_100k": 73,
        "gender": "M"
      },
      {
        "decade": 1980,
        "births_per_100k": 18930,
        "gender": "F"
      },
      {
        "decade": 1980,
        "births_per_100k": 48,
        "gender": "M"
      },
      {
        "decade": 1990,
        "births_per_100k": 10633,
        "gender": "F"
      },
      {
        "decade": 1990,
        "births_per_100k": 22,
        "gender": "M"
      },
      {
        "decade": 2000,
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        "state": "NE",
        "births_per_100k": 2150
      },
      {
        "state": "IN",
        "births_per_100k": 1290
      },
      {
        "state": "MD",
        "births_per_100k": 1290
      },
      {
        "state": "LA",
        "births_per_100k": 1075
      },
      {
        "state": "MN",
        "births_per_100k": 1075
      },
      {
        "state": "KY",
        "births_per_100k": 1075
      },
      {
        "state": "WA",
        "births_per_100k": 1075
      }
    ],
    "decade_graph": [
      {
        "decade": 1970,
        "births_per_100k": 1075,
        "gender": "F"
      },
      {
        "decade": 1980,
        "births_per_100k": 1075,
        "gender": "F"
      },
      {
        "decade": 1990,
        "births_per_100k": 88817,
        "gender": "F"
      },
      {
        "decade": 2000,
        "births_per_100k": 9032,
        "gender": "F"
      }
    ]
  }
]
WITH __stage0 AS (
  SELECT
    group_set,
    __lateral_join_bag.name__0,
    CASE WHEN group_set=0 THEN
      COALESCE(SUM(names.number),0)
      END as total_population__0,
    __lateral_join_bag.state__1,
    floor(((CASE WHEN group_set=1 THEN
      COALESCE(SUM(names.number),0)
      END)/MAX((CASE WHEN group_set=0 THEN
      COALESCE(SUM(names.number),0)
      END)) OVER (PARTITION BY __lateral_join_bag.name__0))*100000) as births_per_100k__1,
    CASE WHEN group_set=2 THEN
      (floor(names.year/10))*10
      END as decade__2,
    __lateral_join_bag.decade__2_string,
    floor(((CASE WHEN group_set=2 THEN
      COALESCE(SUM(names.number),0)
      END)/MAX((CASE WHEN group_set=0 THEN
      COALESCE(SUM(names.number),0)
      END)) OVER (PARTITION BY __lateral_join_bag.name__0))*100000) as births_per_100k__2,
    __lateral_join_bag.gender__2
  FROM `bigquery-public-data.usa_names.usa_1910_current` as names
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,2,1)))
  LEFT JOIN UNNEST([STRUCT(CASE WHEN group_set IN (0,1,2) THEN
    names.name
    END as name__0,
  CASE WHEN group_set=1 THEN
    names.state
    END as state__1,
  CAST(CASE WHEN group_set=2 THEN
    (floor(names.year/10))*10
    END as STRING) as decade__2_string,
  CASE WHEN group_set=2 THEN
    names.gender
    END as gender__2)]) as __lateral_join_bag
  WHERE names.name LIKE 'Mich%'
  GROUP BY 1,2,4,6,7,9
)
SELECT
  name__0 as name,
  ANY_VALUE(CASE WHEN group_set=0 THEN total_population__0 END) as total_population,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
    state__1 as state, 
    births_per_100k__1 as births_per_100k
    ) END IGNORE NULLS  ORDER BY  births_per_100k__1 desc ) as state_map,
  ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(
    decade__2 as decade, 
    births_per_100k__2 as births_per_100k, 
    gender__2 as gender
    ) END IGNORE NULLS  ORDER BY  decade__2 asc ) as decade_graph
FROM __stage0
GROUP BY 1
ORDER BY 2 desc
LIMIT 15

Iconic Names by State and Gender

Calculate the births per 100K for a name in general and a name within a state. Compute and sort by a ratio to figure out relative popularity.

query: names { where: decade < 1970 } -> {
  group_by: name
  group_by: gender
  aggregate: births_per_100k
  nest: by_state is {
    group_by: state
    aggregate: births_per_100k
  }
} -> {
  where: births_per_100k > 50
  group_by: by_state.state
  nest: by_gender is {
    group_by: gender
    nest: by_name is {
      top: 15
      order_by: popularity desc
      group_by: name
      aggregate: popularity is (by_state.births_per_100k - births_per_100k) / births_per_100k
    }
  }
}
QUERY RESULTS
stateby_​gender
AK
gender
F
by_name
namepopularity
Lucy2.969
Colleen2.063
Agnes2.014
Christina1.875
Carla1.696
Terry1.673
Laurie1.309
Kelly1.304
Leslie1.301
Terri1.26
Carrie1.221
Renee1.156
Tracy1.068
Valerie0.915
Vicki0.816
gender
M
by_name
namepopularity
Micheal2.582
Chris1.881
Bryan1.745
Jonathan1.566
Duane1.207
Troy1.196
Jon1.016
Dan0.893
Glen0.891
Arnold0.875
Bradley0.759
Jeffery0.759
Jay0.74
Mike0.711
Joel0.627
AL
gender
F
by_name
namepopularity
Mattie259.755
Willie183.909
Minnie163.589
Lillie152.062
Bessie112.031
Jessie111.355
Nellie102.083
Glenda99.968
Carrie90.324
Annie89.649
Myrtle88.8
Gwendolyn87.121
Ella86.537
Billie83.49
Rosa77.788
gender
M
by_name
namepopularity
Johnnie165.885
Charlie145.746
Jimmie129.067
Cecil106.393
Sam97.635
Eddie76.309
Tommy67.951
Milton59.2
Herman58.229
Jesse55.69
Calvin55.446
Franklin55.443
Leon54.577
Micheal54.436
Willie53.956
AR
gender
F
by_name
namepopularity
Billie99.392
Mattie80.34
Patsy72.556
Willie70.662
Jessie65.5
Glenda64.226
Minnie62.839
Lillie61.708
Myrtle53.527
Margie50.79
Georgia50.773
Bessie48.846
Alma46.879
Maxine46.172
Wilma44.133
gender
M
by_name
namepopularity
Jimmie75.833
Johnnie72.365
Cecil66.714
Tommy55.183
Troy53.821
Charlie44.104
Sam43.25
Herman40.657
Floyd40.073
Calvin40.054
Jimmy39.366
Chester37.281
Eddie36.536
Ronnie35.678
Wesley34.922
AZ
gender
F
by_name
namepopularity
Christina24.696
Rachel21.153
Nellie20.617
Lucy19.609
Rosa18.697
Stella18.526
Terry17.964
Maria15.896
Jessie12.71
Billie11.373
Margie11.323
Victoria11.209
Carla10.857
Terri10.671
Charlene10.136
gender
M
by_name
namepopularity
Manuel121.98
Jose46.234
Gilbert42.279
Dan19.089
Arnold17.625
Bryan16.039
Bob15.855
Chris15.712
Sam15.712
Tom14.813
Tony14.657
Jimmie14.333
Troy13.357
Jonathan13.132
Micheal12.818
CA
gender
F
by_name
namepopularity
Christina275.232
Terry197.218
Leslie175.699
Renee171.172
Rachel155.271
Terri152.096
Carla144.446
Tracy143.324
Andrea141.623
Colleen141.516
Laurie135.136
Victoria132.988
Yvonne131.623
Joy131.17
Valerie129.902
gender
M
by_name
namepopularity
Manuel452.353
Bob243.745
Chris214
Jose210.429
Gilbert209.426
Jeff194.435
Bryan194.02
Dan191.589
Jon191.016
Jonathan159.679
Bradley159.111
Jim155.81
Tom151.147
Micheal144.273
Wesley129.98
CO
gender
F
by_name
namepopularity
Carla24.25
Leona23.154
Christina22.232
Stella21.632
Maxine21.297
Viola20.934
Terry20.709
Kay19.947
Charlene18.924
Terri18.479
Billie17.706
Leslie17.384
Renee17.047
Lucy16.969
Joy16.943
gender
M
by_name
namepopularity
Manuel50.235
Bob36.8
Gilbert35.131
Dan29.5
Chris28.695
Troy26.75
Duane26.224
Sam25.731
Bradley25.185
Glen23.469
Bryan22.431
Wesley22.412
Tom21.76
Jim20.857
Leslie20.759
CT
gender
F
by_name
namepopularity
Maureen42.342
Lucy31.406
Laurie30.63
Lynn27.794
Stella27.158
Emily27.111
Marion27.03
Andrea26.942
Michele24.578
Beth24.304
Wendy23.978
Leslie23.699
Carole21.239
Gertrude21.221
Marcia21.189
gender
M
by_name
namepopularity
Nicholas63.814
Jonathan54.377
Vincent24.255
Theodore21.847
Chester21.328
Frederick20.048
Arnold19.232
Francis19.127
Matthew17.835
Peter17.274
Bryan17.157
Wesley16.275
Leo15.976
Benjamin15.934
Joel15.831
DC
gender
F
by_name
namepopularity
Gwendolyn23.276
Renee18.781
Delores18.296
Leslie15.425
Yvonne15.208
Christina15
Regina14.389
Andrea14.203
Terry13.964
Marsha13.054
Constance12.934
Valerie12.549
Dianne12.382
Carla12.321
Carole11.761
gender
M
by_name
namepopularity
Jonathan25.509
Milton14.95
Jerome14.784
Calvin11.77
Benjamin11.289
Franklin11.049
Bernard10.813
Bryan10.471
Leon9.923
Warren9.724
Alvin9.468
Micheal9.2
Nicholas9.186
Lewis8.958
Jon8.714
DE
gender
F
by_name
namepopularity
Terry5.236
Michele3.9
Andrea3.565
Constance3.526
Regina3.486
Pearl3.267
Valerie3.11
Tracy3.108
Sara3.107
Marian2.949
Carla2.911
Dawn2.877
Christina2.786
Terri2.753
Joanne2.667
gender
M
by_name
namepopularity
Franklin6.049
Jonathan5.019
Harvey4.839
Bryan4.294
Nicholas3.847
Francis3.533
Benjamin3.395
Lewis3.194
Leon2.808
Jay2.636
Barry2.098
Theodore1.959
Alfred1.778
Elmer1.738
Chris1.712
FL
gender
F
by_name
namepopularity
Gwendolyn78.759
Rosa57.652
Lillie53.477
Willie51.74
Mattie49.811
Minnie47.893
Terry46.964
Glenda46.452
Joy44.019
Carla42.054
Marsha39.857
Carrie38.574
Terri36.753
Tracy36.5
Myrtle36.073
gender
M
by_name
namepopularity
Johnnie94.673
Jimmie60.817
Charlie55.612
Cecil53.625
Micheal48.964
Jonathan47.057
Wesley45.02
Troy43.446
Franklin42.148
Eddie40.165
Sam39.923
Bryan39.216
Benjamin38.737
Joel37.305
Calvin37.027
GA
gender
F
by_name
namepopularity
Mattie309.887
Willie188.909
Lillie164.262
Minnie158.464
Rosa137.985
Sara117.293
Bessie111.815
Jessie111.403
Carrie105.441
Nellie103.467
Gwendolyn101.621
Glenda99.113
Annie91.39
Ella91.179
Emily81.722
gender
M
by_name
namepopularity
Charlie236.746
Johnnie190.385
Jimmie107.267
Cecil98.411
Eddie93.515
Sam85.654
Tommy83.207
Franklin76.377
Calvin75.865
Lewis72.958
Milton71.967
Herman68.271
Willie67.794
Joel67.542
Wesley67.412
HI
gender
F
by_name
namepopularity
Charlene13.364
Joy11.075
Emily10.741
Gwendolyn10.259
Renee9.828
Pearl9.667
Colleen9.313
Valerie8.695
Agnes8.625
Elsie8.013
Yvonne7.74
Kay6.912
Wendy6.888
Lynn6.691
Amy6.657
gender
M
by_name
namepopularity
Manuel23.02
Wesley16.863
Gilbert15.787
Benjamin14.303
Leslie13.897
Jonathan11.698
Jon11.413
Milton10.817
Alvin10.403
Calvin9.959
Dean9.779
Bryan9.725
Gordon8.482
Edwin8.309
Clyde7.667
IA
gender
F
by_name
namepopularity
Maxine73.406
Kay62.491
Leona59.038
Marlene45.672
Marcia44.392
Colleen43.672
Delores42.873
Viola41
Carla40.589
Arlene37.354
Marsha36.071
Beth36.043
Wilma35.976
Joy34.434
Dianne33.836
gender
M
by_name
namepopularity
Duane110.017
Bradley69.481
Dean68.429
Bob59.109
Jon56.381
Dan48.304
Arnold47.661
Glen46.234
Darrell45
Jeff42.387
Gene41.366
Tom41.093
Elmer40.585
Jim39.774
Leslie39.431
ID
gender
F
by_name
namepopularity
Maxine14.344
Colleen13.094
Kay12.754
Joy11.509
Carla11.411
Leona10.404
Vickie9.143
Vicki8.782
Billie8.647
Marsha8.304
Terri7.644
Marlene7.343
Glenda7.129
Terry6.8
Beth6.638
gender
M
by_name
namepopularity
Bob17.836
Dan16.411
Glen14.609
Bradley13.889
Wesley13.627
Dean12.948
Duane12.879
Jay12.468
Don11.344
Bryan11.235
Darrell10.943
Jim10.536
Leslie10.431
Gene10.38
Micheal9.727
IL
gender
F
by_name
namepopularity
Leona121.865
Marsha117.893
Renee111.891
Carole111.806
Emily109.019
Marlene107.687
Carla106.125
Kay105.825
Joy105.698
Rosemary105.349
Christina99.929
Marcia98.378
Jill97
Colleen95.328
Charlene94.697
gender
M
by_name
namepopularity
Bradley143.889
Bob125.855
Jerome119.459
Sam115.846
Chester109
Micheal105.691
Dan105.464
Elmer101.492
Bryan99.745
Tom98.147
Leslie96.31
Nicholas93.525
Duane93.155
Glen92.672
Arnold91.732
IN
gender
F
by_name
namepopularity
Marsha87.196
Beth75.971
Carla68.143
Christina66.821
Vickie66.286
Marcia63.581
Kay62.456
Wilma62.373
Sue57.329
Maxine56.297
Joy54.774
Terri51.986
Leona51.788
Vicki49.782
Jill48.545
gender
M
by_name
namepopularity
Bradley87.333
Bryan70.314
Bob67.982
Jeff66.871
Jon65.111
Micheal61.291
Jeffery59.867
Chris58.373
Dan58.107
Troy57.125
Glen53.797
Darrell52.8
Duane52.517
Randall50.747
Chester49.766
KS
gender
F
by_name
namepopularity
Maxine52.344
Leona44.577
Marsha41.321
Wilma39.735
Kay39.105
Billie33.588
Carla33.446
Vera32.75
Glenda32.032
Viola28.328
Vickie28.127
Joy27
Delores26.606
Marcia24.892
Terri24.521
gender
M
by_name
namepopularity
Bob53.473
Duane50.431
Bradley50.074
Wesley37.039
Cecil36.464
Glen36.391
Elmer34.415
Leslie34.121
Darrell33.8
Vernon32.179
Troy32.161
Jimmie31.083
Bryan30.196
Jon29.81
Dean29.351
KY
gender
F
by_name
namepopularity
Myrtle95.673
Billie91.235
Alma77.121
Nellie76.65
Margie74.532
Wilma73.47
Stella71.789
Mattie71.019
Minnie70.946
Bessie68.6
Georgia66.621
Vickie62.921
Patsy59.958
Lucy58.922
Ella58.687
gender
M
by_name
namepopularity
Cecil102.982
Chester75.734
Elmer74.723
Arnold63.75
Jimmie63.117
Darrell60.329
Ronnie57.943
Troy57.339
Clyde55.267
Leslie53.259
Franklin52.016
Johnnie51.077
Herman46.286
Jesse45.167
Glen43.594
LA
gender
F
by_name
namepopularity
Gwendolyn93.241
Lillie87.262
Mattie77.774
Willie76.623
Myrtle72.818
Ella71.806
Dianne69.655
Jessie66.371
Glenda66
Billie64.059
Bessie63.862
Margie62.016
Leona61.558
Rosa55.985
Pearl54.183
gender
M
by_name
namepopularity
Johnnie81.481
Alvin78.753
Troy77.786
Sam76.135
Milton74.35
Herman69.8
Jimmie67.333
Micheal65.764
Calvin64.027
Bryan58.451
Charlie53.552
Leon50.987
Lester46.899
Eddie44.763
Chris44.034
MA
gender
F
by_name
namepopularity
Maureen153.342
Dianne102.273
Constance73.145
Jeanne70.713
Gertrude70.455
Emily70.222
Carole68.418
Marion66.614
Eileen66
Andrea64.348
Joanne63.858
Laurie62.086
Marcia61.797
Stella60.333
Christina55.607
gender
M
by_name
namepopularity
Jonathan100.019
Manuel96.137
Leo89.28
Nicholas88.661
Francis74.567
Chester62.328
Frederick52.904
Arnold48.786
Warren46.943
Alfred46.452
Philip43.021
Barry39.424
Gordon39.169
Joel39.102
Gilbert38.393
MD
gender
F
by_name
namepopularity
Terry43.6
Christina35.161
Regina34.333
Delores32.352
Agnes31.931
Emily31.093
Gwendolyn30.638
Carole29.134
Michele27.133
Pearl26.833
Leslie26.822
Constance25.908
Valerie25.756
Renee25.578
Myrtle25.473
gender
M
by_name
namepopularity
Milton43.717
Franklin43.066
Bryan40.02
Jerome35.068
Jonathan34.849
Vernon32.936
Calvin32.392
Elmer31.708
Gilbert31.295
Bernard26.888
Benjamin26.618
Wesley24.373
Herman23.729
Harvey23.371
Lewis23.069
ME
gender
F
by_name
namepopularity
Rachel25.915
Constance23.329
Dianne16.418
Arlene15.962
Laurie15.457
Leona14.808
Maxine14.016
Pauline13.813
Charlene13.561
Terry13.491
Gertrude13.117
Marion12.297
Lorraine10.516
Marlene10.448
Pearl9.767
gender
M
by_name
namepopularity
Arnold19.768
Leo17.402
Jonathan14.453
Leslie13.293
Gilbert12.311
Lewis12.222
Gordon11.964
Joel11.627
Chester11.203
Philip10.507
Wesley10.49
Jon9.81
Duane9.534
Leon9.462
Franklin9.41
MI
gender
F
by_name
namepopularity
Kay119.947
Renee109.891
Leona109.5
Colleen109.156
Marlene100.537
Dianne95.182
Marcia93.581
Beth91.826
Jill91.74
Maxine87.719
Marsha86.179
Terry85.291
Carole83.701
Dawn81.802
Charlene80.652
gender
M
by_name
namepopularity
Duane162.569
Dan106.161
Bradley100.407
Bryan98.333
Gordon94.253
Chris91.492
Micheal91.036
Jon88.222
Arnold87.071
Bob85.891
Jeff85.065
Tom83.213
Wesley80.314
Jerome75.176
Jeffery73.289
MN
gender
F
by_name
namepopularity
Colleen77.25
Marlene68.537
Delores65.887
Leona65.019
Kay64.018
Dianne62.382
Renee55.391
Jill49.468
Myrtle47
Arlene46.304
Laurie46.284
Marian45.385
Beth45.29
Joy43.189
Jeanne41.553
gender
M
by_name
namepopularity
Duane146.138
Bradley134.037
Arnold84.679
Jon82.873
Jerome80.865
Dean77.61
Joel70.237
Gordon62.843
Todd60.105
Vernon54.359
Wesley51.039
Leslie49.569
Dan49.446
Troy49.429
Harvey48.387
MO
gender
F
by_name
namepopularity
Glenda79.516
Wilma76.337
Carla71.661
Billie69.647
Leona69.231
Marsha68.696
Georgia65.47
Maxine61.516
Terry60.582
Joy54.283
Vickie53.206
Myrtle53.018
Patsy52.681
Nellie51.95
Christina51.464
gender
M
by_name
namepopularity
Bob94.509
Jimmie71
Elmer70.969
Cecil70.571
Glen62.656
Bryan59.647
Wesley57.275
Leslie55.966
Micheal54.873
Darrell54.771
Vernon53.731
Lester52.971
Floyd50.854
Bradley50.796
Chris50.542
MS
gender
F
by_name
namepopularity
Mattie205.642
Willie167.091
Lillie150.8
Minnie146.911
Jessie114.532
Bessie108.477
Ella82.09
Carrie73.338
Georgia70.924
Annie68.708
Billie66.196
Ida59.566
Myrtle57.382
Glenda55.258
Margie54.645
gender
M
by_name
namepopularity
Johnnie183.288
Jimmie139.167
Charlie123.149
Sam106.731
Eddie83.237
Tommy56.195
Cecil56.036
Willie54.855
Milton54.133
Calvin47.919
Herman46.9
Lee44.608
Leon42.667
Clyde39.867
Curtis39.864
MT
gender
F
by_name
namepopularity
Colleen14.859
Leona13.154
Marlene11.537
Kay11.386
Carla10.054
Billie9.392
Myrtle9.036
Maxine8.891
Terry8.764
Georgia7.864
Agnes7.833
Joy7.811
Laurie7.802
Viola7.541
Marian7.179
gender
M
by_name
namepopularity
Duane19.724
Dan18.25
Bob16.764
Wesley13.961
Leslie13.931
Jim10.774
Tom10.627
Gordon10.373
Bradley10.148
Dean10.143
Glen9.969
Arnold9.25
Bill8.851
Gene8.845
Bryan8.49
NC
gender
F
by_name
namepopularity
Mattie156.019
Minnie146.696
Lillie139.415
Myrtle132.927
Rachel125.322
Rosa123.53
Nellie121.417
Bessie111.985
Ella103.642
Margie103.016
Jessie99.548
Lucy97.516
Carrie96.779
Glenda95.387
Gwendolyn94.707
gender
M
by_name
namepopularity
Charlie174
Johnnie166.231
Cecil101.964
Clyde95.827
Franklin87.689
Jimmie86.033
Harvey85.355
Ronnie82.678
Milton76.983
Jesse75.536
Herman75.343
Calvin74.068
Tommy68.976
Lewis68.514
Micheal68.273
ND
gender
F
by_name
namepopularity
Leona26.558
Delores22.915
Viola20.885
Marlene20.657
Myrtle19.982
Agnes18
Colleen15.359
Arlene14.354
Pearl14.267
Kay13.737
Esther11.981
Marian11.564
Maxine11.547
Elsie11.192
Dianne10.873
gender
M
by_name
namepopularity
Duane57.5
Arnold28.304
Bradley26.37
Vernon19.846
Dean19.805
Wesley19.725
Leslie19.431
Elmer19.185
Jerome19.041
Gordon18.614
Alvin15.143
Leo15
Lloyd14.598
Jon12.984
Harvey12.968
NE
gender
F
by_name
namepopularity
Maxine40.797
Leona39.442
Kay31.316
Delores26.789
Marlene26.507
Viola25.426
Colleen22.281
Marcia20.014
Wilma19.988
Arlene19.316
Vera18.632
Dianne18.036
Joy17.868
Marian16.423
Beth16.174
gender
M
by_name
namepopularity
Duane57.207
Bradley38
Bob36.909
Dean32.532
Glen29.266
Gene25.845
Dan25.839
Arnold25.804
Wesley25.647
Elmer25.077
Leslie24.5
Jim22.643
Jon21.603
Vernon21.603
Bryan21.451
NH
gender
F
by_name
namepopularity
Rachel13.814
Constance10.816
Pauline9.561
Dianne7.364
Maureen7.013
Marion6.96
Laurie6.914
Lorraine6.828
Leona6.654
Gertrude6.455
Stella6.123
Jeanne6.021
Arlene5.962
Wendy5.876
Beatrice5.347
gender
M
by_name
namepopularity
Leo12.951
Jonathan12.509
Bradley6.944
Gordon6.687
Leon6.641
Jon6.143
Arnold5.875
Norman5.378
Chester4.531
Leslie4.483
Nicholas4.441
Bryan4.353
Theodore4.071
Philip3.89
Warren3.828
NJ
gender
F
by_name
namepopularity
Maureen93.829
Michele76.533
Eileen72.386
Emily68.704
Stella66.965
Christina64.804
Colleen63.422
Carole63.045
Arlene61.506
Andrea61.043
Renee57.078
Regina55.861
Marion55.416
Valerie51.988
Jill51.987
gender
M
by_name
namepopularity
Nicholas160.492
Jonathan69.755
Vincent69.218
Theodore51.327
Chester47.172
Arnold44.607
Benjamin43.724
Barry42.424
Milton41.917
Frederick40.648
Sam40.462
Warren39.851
Victor39.053
Chris38.661
Jerome38.365
NM
gender
F
by_name
namepopularity
Lucy35.25
Stella27.614
Viola22.525
Patsy15.958
Rosa15.848
Carla15.75
Christina15.321
Nellie15.217
Maria14.922
Emily14.574
Rachel13.864
Billie13.529
Terry12.509
Margie12.242
Yvonne12.156
gender
M
by_name
namepopularity
Manuel122.608
Jose76.338
Gilbert52.836
Sam18.154
Tommy17.927
Tony17.071
Chris15.576
Jimmie15.35
Johnnie14.481
Dan14.143
Tom13.707
Leo13.098
Benjamin12.987
Herman12.3
Charlie12.299
NV
gender
F
by_name
namepopularity
Leslie2.973
Terry2.945
Christina2.929
Tracy2.703
Carla2.161
Colleen1.922
Terri1.808
Valerie1.732
Laurie1.728
Jill1.623
Renee1.609
Kelly1.609
Kim1.505
Andrea1.493
Tina1.415
gender
M
by_name
namepopularity
Troy3.768
Bryan2.961
Jon2.73
Chris2.525
Micheal2.345
Jeff2.258
Dan1.911
Mike1.675
Jim1.631
Bradley1.444
Todd1.274
Tom1.2
Dean1.169
Bob1.127
Bill1.117
NY
gender
F
by_name
namepopularity
Maureen306.789
Eileen237.594
Emily212.407
Renee203.531
Arlene200.013
Christina196.536
Andrea194.826
Lucy190.141
Gertrude187
Marion178.168
Carole177.955
Michele175.856
Regina174.153
Colleen170.547
Pearl161.633
gender
M
by_name
namepopularity
Nicholas446.712
Jonathan272.075
Vincent241.3
Arnold230.714
Joel202.441
Milton195.367
Sam186.269
Jose177.883
Jerome169.392
Harvey151.645
Bernard151.533
Victor141.351
Benjamin139.224
Martin137.629
Edwin136.897
OH
gender
F
by_name
namepopularity
Beth134.232
Marsha127.964
Christina124.482
Kay116.053
Carla114.804
Joy104.679
Marlene104.03
Marcia101.595
Vickie100.81
Sue100.306
Carole98.373
Jill95.623
Dianne94.709
Renee94.516
Charlene93.894
gender
M
by_name
namepopularity
Dan123.839
Bryan123.373
Jeff117.468
Bradley114.37
Bob110.855
Tom104.707
Jon101.444
Duane101.034
Chris98.475
Jonathan97.453
Nicholas93.593
Jeffery91.916
Elmer90.338
Micheal87.764
Chester86.203
OK
gender
F
by_name
namepopularity
Billie152.647
Patsy72.903
Glenda69.032
Leona59.538
Joy58.774
Wilma56.675
Maxine54.109
Margie53.194
Myrtle52.527
Georgia49.318
Jessie49.177
Vera46.603
Minnie46.036
Carla45.089
Viola43.148
gender
M
by_name
namepopularity
Jimmie98.633
Cecil90.036
Bob85.182
Tommy65.11
Troy63.125
Johnnie61.25
Wesley61
Glen55.078
Bill53.766
Floyd52.22
Elmer50.6
Gene42.887
Vernon42.628
Don42.366
Darrell41.643
OR
gender
F
by_name
namepopularity
Colleen24.406
Kay22.246
Joy20.208
Carla20.089
Terry19.655
Maxine19.641
Leslie18.041
Laurie17.568
Christina17.179
Terri16.507
Leona15.942
Vickie15.778
Jill15.169
Vicki15.057
Marlene14.925
gender
M
by_name
namepopularity
Bob39.036
Dan37.839
Bradley30.037
Jeff29.065
Wesley28.275
Duane28.121
Jim26.119
Jon25.762
Troy24.589
Micheal24.382
Chris22.695
Tom20.693
Leslie20.121
Bryan20.118
Gordon20
PA
gender
F
by_name
namepopularity
Stella201.982
Agnes167.222
Leona163.346
Marlene159.612
Marian156
Regina155.347
Beth149.362
Emily147.426
Pearl146.033
Carole143.955
Sara139.48
Arlene134.038
Gertrude132.922
Christina129.446
Colleen125.453
gender
M
by_name
namepopularity
Nicholas231.797
Chester133.328
Barry132.457
Elmer130.323
Franklin121.426
Jonathan117.472
Jay113.494
Francis107.707
Bernard105.374
Theodore105.01
Lester103.783
Lewis101.25
Bryan101.157
Harvey100.323
Benjamin98.276
RI
gender
F
by_name
namepopularity
Maureen19.816
Constance13.605
Dianne13.182
Jeanne11.617
Colleen10.891
Gertrude10.571
Emily10.389
Lucy9.703
Laurie9.481
Eileen8.861
Leslie8.479
Agnes8.194
Gail8.05
Marion7.98
Rachel7.881
gender
M
by_name
namepopularity
Manuel26.49
Leo17.78
Nicholas16.424
Jonathan9.962
Alfred9.937
Vincent9.673
Gilbert8.902
Frederick7.208
Francis7.18
Milton6.267
Norman6.063
Arnold5.768
Gordon5.024
Ernest4.85
Peter4.83
SC
gender
F
by_name
namepopularity
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Rosa140.652
Lillie137.908
Minnie111.518
Carrie107.162
Willie97.377
Jessie93.677
Bessie83.369
Ella78.03
Sara67.893
Margie61.742
Viola59.279
Myrtle58.909
Nellie58.017
Emily56.759
gender
M
by_name
namepopularity
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Charlie89.269
Sam85.75
Franklin64.574
Benjamin59.566
Jimmie59.133
Leroy53.519
Lewis51.014
Cecil50.893
Calvin48.068
Leon42.808
Clyde42.8
Herman41.757
Eddie41.216
Tommy39.549
SD
gender
F
by_name
namepopularity
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Delores20.803
Viola19.361
Kay18.509
Maxine18.344
Marlene15.97
Dianne14.291
Arlene13.013
Colleen12.688
Myrtle12.655
Carla11.482
Pearl10.917
Agnes10.611
Joy9.925
Vera9.397
gender
M
by_name
namepopularity
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Bradley22.056
Arnold20.357
Dean18.506
Wesley17.157
Leslie16.81
Elmer15.892
Darrell14.729
Vernon13.897
Gene13.732
Alvin13.351
Glen13.266
Troy13.214
Harvey12.839
Todd12.558
TN
gender
F
by_name
namepopularity
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Billie115.882
Glenda90.403
Willie89.221
Minnie88.143
Patsy85.125
Nellie78.283
Myrtle78.091
Georgia75.788
Vickie74.508
Jessie72.21
Wilma72.205
Bessie71.4
Lillie70.277
Margie68.694
gender
M
by_name
namepopularity
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Charlie79.642
Sam77.923
Johnnie74.577
Tommy70.244
Jimmie69.167
Clyde65.8
Ronnie65.241
Micheal60.673
Troy57.429
Franklin55.984
Ricky53.062
Herman51.414
Jimmy48.91
Bobby48.617
TX
gender
F
by_name
namepopularity
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Rosa301.652
Maria227.461
Patsy218.653
Glenda186.661
Margie172.984
Rachel151
Alma150.258
Minnie148.893
Willie148.325
Lillie144.523
Mattie143.509
Joy137.509
Jessie133.758
Carla130.393
gender
M
by_name
namepopularity
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Jose592.429
Johnnie200.327
Gilbert195.902
Jesse188.06
Jimmie186.967
Tommy181.415
Cecil173
Troy153.857
Sam151.385
Wesley150.745
Joel135.966
Bryan133.902
Charlie126.642
Milton121.867
UT
gender
F
by_name
namepopularity
Colleen30.781
Vickie19.524
Joy18.528
Beth17.913
Kay17.351
Maxine17.016
Jill16.896
Wendy16.112
Marsha15.768
Leslie15.589
Annette13.718
Terry12.255
Carla12.179
Laurie11.383
Cindy10.931
gender
M
by_name
namepopularity
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Troy30.268
Jay27.169
Dan26.143
Bryan25.882
Bradley23.056
Don22.505
Jeff22
Dean19.974
Chris19.034
Duane18.517
Gordon17.53
Bob16.655
Jon15.651
Wesley15.549
VA
gender
F
by_name
namepopularity
Rosa83.985
Myrtle79.964
Nellie79.717
Lucy79.688
Mattie73.604
Bessie70
Pearl66.083
Ella64.224
Elsie62.756
Lillie59.938
Emily59.537
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Rachel57.695
Carrie55.632
Alma55.348
gender
M
by_name
namepopularity
Charlie96.03
Cecil85.089
Lewis75.181
Franklin72.885
Calvin66.081
Wesley60.059
Herman59.929
Milton57.8
Clyde54.88
Johnnie54.846
Harvey50.968
Benjamin47.079
Leslie45.793
Alvin45.442
Troy44.607
VT
gender
F
by_name
namepopularity
Marion7.891
Leona6.904
Rachel6.407
Laurie6.272
Arlene5.316
Dianne5.018
Pauline4.667
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Colleen4.016
Wendy4
Gertrude3.987
Constance3.737
Lorraine3.672
Beatrice3.663
Pearl3.4
gender
M
by_name
namepopularity
Gordon8.024
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Todd3
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WA
gender
F
by_name
namepopularity
Colleen52.375
Kay37.86
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Terry32.873
Laurie32.63
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Terri30
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gender
M
by_name
namepopularity
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Glen34.984
WI
gender
F
by_name
namepopularity
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gender
M
by_name
namepopularity
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Glen47
WV
gender
F
by_name
namepopularity
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Vickie32.571
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gender
M
by_name
namepopularity
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WY
gender
F
by_name
namepopularity
Kay4.667
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Billie3.529
Colleen3.359
Carla3.018
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gender
M
by_name
namepopularity
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Dean3.714
Bill3.585
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Jon3.429
Bradley3.37
Duane3.31
Leslie3.207
Don3.161
Jay2.831
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    "state": "WV",
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      {
        "gender": "F",
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          {
            "name": "Nellie",
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          {
            "name": "Myrtle",
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]
WITH __stage0 AS (
  SELECT
    group_set,
    __lateral_join_bag.name__1,
    __lateral_join_bag.gender__1,
    floor(((CASE WHEN group_set=1 THEN
      COALESCE(SUM(names.number),0)
      END)/MAX((CASE WHEN group_set=0 THEN
      COALESCE(SUM(names.number),0)
      END)) OVER ())*100000) as births_per_100k__1,
    __lateral_join_bag.state__2,
    floor(((CASE WHEN group_set=2 THEN
      COALESCE(SUM(names.number),0)
      END)/MAX((CASE WHEN group_set=1 THEN
      COALESCE(SUM(names.number),0)
      END)) OVER (PARTITION BY __lateral_join_bag.name__1, __lateral_join_bag.gender__1))*100000) as births_per_100k__2
  FROM `bigquery-public-data.usa_names.usa_1910_current` as names
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,2,1)))
  LEFT JOIN UNNEST([STRUCT(CASE WHEN group_set IN (1,2) THEN
    names.name
    END as name__1,
  CASE WHEN group_set IN (1,2) THEN
    names.gender
    END as gender__1,
  CASE WHEN group_set=2 THEN
    names.state
    END as state__2)]) as __lateral_join_bag
  WHERE ((floor(names.year/10))*10)<1970
  GROUP BY 1,2,3,5
)
, __stage1 AS (
  SELECT
    name__1 as name,
    gender__1 as gender,
    ANY_VALUE(CASE WHEN group_set=1 THEN births_per_100k__1 END) as births_per_100k,
    ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(
      state__2 as state, 
      births_per_100k__2 as births_per_100k
      ) END IGNORE NULLS  ORDER BY  births_per_100k__2 desc ) as by_state
  FROM __stage0
  WHERE group_set NOT IN (0)
  GROUP BY 1,2
  ORDER BY 3 desc
)
, __stage2 AS (
  SELECT
    group_set,
    CASE WHEN group_set IN (0,1,2) THEN
      by_state_0.state
      END as state__0,
    CASE WHEN group_set IN (1,2) THEN
      base.gender
      END as gender__1,
    CASE WHEN group_set=2 THEN
      base.name
      END as name__2,
    CASE WHEN group_set=2 THEN
      ((by_state_0.births_per_100k-base.births_per_100k))/base.births_per_100k
      END as popularity__2
  FROM __stage1 as base
  LEFT JOIN UNNEST(base.by_state) as by_state_0
  CROSS JOIN (SELECT row_number() OVER() -1  group_set FROM UNNEST(GENERATE_ARRAY(0,2,1)))
  WHERE base.births_per_100k>50
  GROUP BY 1,2,3,4,5
)
, __stage3 AS (
  SELECT 
    CASE WHEN group_set=2 THEN 1  ELSE group_set END as group_set,
    state__0 as state__0,
    CASE WHEN group_set IN (1,2) THEN
      gender__1
      END as gender__1,
    ARRAY_AGG(CASE WHEN group_set=2 THEN STRUCT(
      name__2 as name, 
      popularity__2 as popularity
      ) END IGNORE NULLS  ORDER BY  popularity__2 desc  LIMIT 15) as by_name__1
  FROM __stage2
  GROUP BY 1,2,3
)
SELECT
  state__0 as state,
  ARRAY_AGG(CASE WHEN group_set=1 THEN STRUCT(
    gender__1 as gender, 
    by_name__1 as by_name
    ) END IGNORE NULLS  ORDER BY  gender__1 asc ) as by_gender
FROM __stage3
GROUP BY 1
ORDER BY 1 asc